Ecological and Confined Domain Ontology Construction Scheme Using Concept Clustering for Knowledge Management

Abstract

Knowledge management in a structured system is a complicated task that requires common, standardized methods that are acceptable to all actors in a system. Ontology, in this regard, is a primary element and plays a central role in knowledge management, interoperability between various departments, and better decision making. The ontology construction for structured systems comprises logical and structural complications. Researchers have already proposed a variety of domain ontology construction schemes. However, these schemes do not involve some important phases of ontology construction that make ontologies more collaborative. Furthermore, these schemes do not provide details of the activities and methods involved in the construction of an ontology, which may cause difficulty in implementing the ontology. The major objectives of this research were to provide a comparison between some existing ontology construction schemes and to propose an enhanced ecological and confined domain ontology construction (EC-DOC) scheme for structured knowledge management. The proposed scheme introduces five important phases to construct an ontology, with a major focus on the conceptualizing and clustering of domain concepts. In the conceptualization phase, a glossary of domain-related concepts and their properties is maintained, and a Fuzzy C-Mean soft clustering mechanism is used to form the clusters of these concepts. In addition, the localization of concepts is instantly performed after the conceptualization phase, and a translation file of localized concepts is created. The EC-DOC scheme can provide accurate concepts regarding the terms for a specific domain, and these concepts can be made available in a preferred local language.

Ontology-based classification method using statistical and symbolic approaches for plant diseases detection in Agriculture

“…In [11], the authors reviewed a variety of techniques related to direct methods, including: the Polymerase chain reaction (PCR) technique for finding microorganism counts in samples. Detecting plant diseases through laboratory-based methods is highly accurate, but it is time consuming, requires specialized equipment, and requires expertise [12]. A biomarker-based disease detection method and a property-based disease detection method are indirect methods for detecting diseases.…”

An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study

“…ON-ODM S Some criteria have been collected from [13,15,29,31,35,36,44], in addition to another set suggested in the current study. An outline of these criteria is given below.…”

“…For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process [15]. Further challenges and possible future directions have been proposed in [16].…”

“…Ontology development methodology provides guidelines about the organization of activities and tasks, the definition of transitions between them, the selection of methods applied in each task, recommendation of the most suitable tools, and so on. Despite the fact that a variety of methodologies have been proposed [4,15,17], there are still many open issues about ontology development that have yet to be answered [13]. New methodologies continue to be introduced as they propose ontology development from different perspectives and focus on different aspects [15].…”

“…Despite the fact that a variety of methodologies have been proposed [4,15,17], there are still many open issues about ontology development that have yet to be answered [13]. New methodologies continue to be introduced as they propose ontology development from different perspectives and focus on different aspects [15]. In brief, there is no common consensus on an ideal methodology; however, the purpose of developing the ontology may aid in the selection of the most suitable methodology.…”